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May 2018

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Cluster Analysis of Endogenous HER2 and HER3 Receptors in SKBR3 Cells
在SKBR3细胞中内源性HER2和HER3受体的聚类分析   

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Abstract

The Human Epidermal Growth Factor Receptor (HER) family of receptor tyrosine kinases consists of four, single pass, transmembrane receptor homologs (HER1-4) that act to regulate many critical processes in normal and tumor cells. HER2 is overexpressed in many tumors, and the deregulated proliferation of cancerous cells is driven by cooperation with its preferred receptor partner, HER3. The assessment of the in-situ organization of tagged HER2 and HER3 using super-resolution microscopy reveals quantitative Single Molecule Localization Microscopy (SMLM) as an ideal bioanalytical tool to characterize receptor clusters. Clustering of receptors is an important regulatory mechanism to prime cells to respond to stimuli so, to understand these processes, it is necessary to measure parameters such as numbers of clusters, cluster radii and the number of localizations per cluster for different perturbations. Previously, Fluorescence Localization Imaging with Photobleaching (FLImP), another nanoscale, single-molecule technique, characterized the oligomerization state of HER1 [or Epidermal Growth Factor Receptors (EGFR)] in cell membranes. To achieve an unprecedented resolution (< 5 nm) for inter-molecular separations in EGFR oligomers using FLImP, very few receptors are tagged, and so this method is unsuitable for measurements of whole receptor populations in cancer cells where receptors are frequently upregulated. Here, in order to detect all receptors involved in cluster formation, we saturate endogenous HER2 and HER3 membrane receptors with ligands at a 1:1 dye to protein ratio, in the presence or absence of therapeutic drugs (lapatinib or bosutinib). This is performed in the commonly used breast cancer cell line model SKBR3 cells, where there are ~1.6 million HER2 receptors/cell and 10,000-40,000 HER3 receptors/cell. The basal state of these receptors is studied using HER2- or HER3-specific Affibodies, and likewise, the active state is probed using the natural HER3 ligand, Neuregulin-beta1 (NRGβ1). Stochastic Optical Reconstruction Microscopy (STORM), one form of SMLM, was used here to image cells, which were chemically fixed to minimize image blurring and provide data (x and y coordinates and standard deviation of the measured localizations) for cluster analysis. Further analysis can also determine proportions of receptor colocalizations. Our findings show that lapatinib-bound HER2, complexed with HER3 via a non-canonical kinase dimer structure, induces higher order oligomers. We hypothesized that nucleation of receptors creates signaling platforms that explain the counterintuitive, increase in cell proliferation upon ligand binding, in the presence of the HER2-inhibitor lapatinib.

Keywords: Epidermal growth factor receptor (EGFR) (表皮生长因子受体), Human epidermal growth factor receptor 2 (HER2) (人表皮生长因子受体2), Human epidermal growth factor receptor 3 (HER3) (人表皮生长因子受体3), Single-molecule localization microscopy (SMLM) (单分子定位显微术), Stochastic optical reconstruction microscopy (STORM) (随机光学重建显微法), Lapatinib (拉帕替尼), Cluster analysis (聚类分析)

Background

Homo- and hetero-protein clustering facilitates the efficient regulation of signaling processes in cells (Sourjik and Armitage, 2010; Nussinov, 2013; Truong-Quang and Lenne, 2014; Recouvreux and Lenne, 2016). Mechanisms include, but are not limited to, the sequestering of proteins (e.g., GPI-anchored folate receptors [Mayor et al., 1994]), creation of docking sites (e.g., Syntaxin/SNARE family proteins [Sieber et al., 2007]), steric hindrance of interactions (e.g., Rictor and mSin1 in mTOR complex-2 [Chen et al., 2018]), aggregation of misfolded proteins (e.g., amyloid fibrils [Adamcik et al., 2010]), an accelerated response to pre-equilibrium signals during chemotaxis (Ventura et al., 2014), regulation of integrins at focal adhesions (Spiess et al., 2018) and formation of signaling cascades (e.g., GPCR signaling [Maurel et al., 2008; Jonas et al., 2015]). Specifically, we focus on the analysis of membrane receptor clustering/oligomerization to investigate molecular rearrangements due to drug binding. We highlight the importance of mutual cooperation resulting in an increased signaling output (Claus et al., 2018), a theme likely to be common in signaling cascades. In addition, we suggest that this protocol is generally applicable to many biological questions relating to protein clusters. Minor modifications to the sample preparation should allow localization measurements of any protein(s), either immobilized on glass or expressed in bacteria or other (e.g., mammalian) cells. Models of protein interactions and the consideration of differences between wild type proteins and equivalents comprising pertinent mutations can often lead to a more comprehensive molecular mechanism. We believe, therefore that the quantitative clustering and colocalization data delivered by this protocol advances our understanding of native and disease states even further.

The proteins of interest in this study, HER2, and HER3, are two members of the HER family of receptor tyrosine kinases (Normanno et al., 2006; Eccles, 2011). HER2 is overexpressed in many tumors, and the deregulated proliferation of cancerous cells is driven by cooperation with its preferred receptor partner, HER3 (Holbro et al., 2003). SMLM techniques (e.g., STORM for photoactivatable fluorophores or PALM for genetically-expressed tagged proteins) can image single fluorescently-tagged protein(s), like receptors, at the plasma membrane or inside the cell (Nicovich et al., 2017). STORM is a popular imaging modality thanks to a relatively simple experimental setup and procedure (Rust et al., 2006) and the availability of free, open-source software for processing, analysis and visualization of the data (Ovesný et al., 2014). We chose STORM for our application because fluorophore blinking is induced stochastically, and localization achieved one group of fluorophores at a time, the process being continued and signals accumulated until a detailed image is built. Crucially, the STORM process makes the technique compatible with saturated labeling of all endogenous HER2 protein using an Affibody ligand that is small, specific and uniformly conjugated to Alexa 488 at a ratio of 1:1 Affibody:dye (Wikman et al., 2004; Eigenbrot et al., 2010). Other HER protein affibodies are available (Friedman et al., 2007; Nordberg et al., 2007; Kronqvist et al., 2010; Gostring et al., 2012;). Cluster analysis of SMLM data can, in turn, reveal the number of molecules per cluster and the cluster size. The post analysis software we used is readily available from the research teams led by Dylan Owen (Griffié et al., 2016) and Katharina Gaus (Pageon et al., 2016) in combination with the custom written tools provided here. If required, it may be possible to use SMLM and cluster analysis to study these receptors in live cells (an extension to the analysis method is given in Griffié et al., 2018) however this has not been implemented as yet.

We also wanted to characterize protein-protein interactions within clusters, information that cannot be obtained via cluster analysis alone. Fluorescence Resonance Energy Transfer measurements using Fluorescence Lifetime Imaging Microscopy (FRET-FLIM) was also used extensively in Claus et al. to measure inhibitor-induced heterotypic interactions between HER2 and HER3 and their respective mutants (Claus et al., 2018). FRET-FLIM data complement the analysis of clusters by reporting the stability of heterodimeric complexes. However, since the length scale of FRET is 2-8 nm, the method cannot be used to report on the typically larger cluster sizes, the combination is important in providing a deeper organizational view of these receptors.

Other imaging techniques for measuring protein-protein interactions and clustering can be used in combination with cluster analysis to provide complementary information and provide a more complete picture of the underlying processes from the dynamic and structural point of view. These include two-color Single Particle Tracking (SPT), which reports the number of pairwise receptor particle interactions and the duration of these interactions (Low-Nam et al., 2011; Needham et al., 2013) allowing the distinction between multimers and dimers. However, SPT images single molecules or particles as they move on live cells and the associated blurring means that SPT often does not have enough resolution to determine the number of receptors in the “particles” imaged as they move. FLImP is an ideal method to measure precise receptor separations on static particles (Needham et al., 2013). In model cell systems the expression level of receptors can be optimized. However HER2 is highly overexpressed in the SKBR3 cell line (Eccles, 2011; Shankaran et al., 2013), and labeling would need to be kept at low levels. Nevertheless, FLImP should be able to provide information complementary to FRET on receptor-receptor interactions and the geometry of the complexes. Stimulated Emission Depletion (STED) microscopy, a distinct but complementary super-resolution imaging technique to STORM, can measure protein clusters ~50 nm in diameter. Utilizing this resolution for fixed, immuno-labeled, cell samples, Kellner et al. measured for the first time changes in the size distribution of nicotinic acetylcholine receptor (AChR) nanoclusters (Kellner et al., 2007) and Dzyubenko more recently employed this method to reveal the density of marker proteins in postsynaptic neurons (Dzyubenko et al., 2016). The direct comparison of nearest neighbor distance for active integrins derived from both techniques illustrates that STORM benefits from higher resolution than STED (Spiess et al., 2018). The latter technique, however, could extend cluster analysis to live cells and achieve higher temporal resolution. If a robust fluorophore calibration could be made, number and brightness analysis would provide stoichiometric and dynamic measurements from STED data of live cells.

Materials and Reagents

  1. 75 cm2 filter cap cell culture flasks (Thermo Fisher Scientific, NuncTM EasYFlaskTM, catalog number: 156499)
  2. 35 mm Glass Bottom Dishes, High Tolerance 1.5 Coverslip, 14 mm Glass Diameter (MatTek Corporation, catalog number: P35G-0.170-14-C) 
  3. 5 ml individually packed sterile Serological pipettes (Corning incorporated, Costar StripetteTM, catalog number: 4487)
  4. 10 ml individually packed sterile Serological pipettes (Corning incorporated, Costar StripetteTM, catalog number: 4488)
  5. 25 ml individually packed sterile Serological pipettes (Corning incorporated, Costar StripetteTM, catalog number: 4489)
  6. PD10 purification columns (GE Healthcare, catalog number 17085101)
  7. 24-well plates (NunclonTM, catalog number: 143982)
  8. Aluminum foil (Terinex, catalog number: 11330)
  9. SK-BR-3 [SKBR3] (ATCC® HTB-30TM) cells (LGC standards) (ATCC, catalog number: HTB-30)
  10. Bovine Serum Albumin (BSA) (Sigma-Aldrich, catalog number: A1470) (Store at 4 °C)
  11. RPMI 1640 media without phenol red (Thermo Fisher Scientific, GibcoTM, catalog number: 11835063) (Store at 4 °C)
  12. Fetal Bovine Serum (FBS) (Thermo Fisher Scientific, GibcoTM, catalog number: 10270) (Aliquot and store at -20 °C)
  13. L-Glutamine (Thermo Fisher Scientific, GibcoTM, catalog number: 25030024) (Aliquot and store at -20 °C)
  14. Lapatinib (BioVision, catalog number: 1624) (Aliquot and store at -20 °C)
  15. Bosutinib (Sigma-Aldrich, catalog number: PZ0192) (Aliquot and store at -20 °C)
  16. Neuregulin beta-1 (NRGβ1) (Peprotech, catalog number: 100-03) (Aliquot and store at -20 °C)
  17. Anti-HER2, imaging agent (Affibody Incorporated, catalog number: 10.1861.01.0005) (Store powder at 4 °C, once reconstituted, aliquot and store at -20 °C)
  18. HER3 Affibody ligand (Plasmid was a gift from John Löfblom [Kronqvist et al., 2011], a protein expressed and purified in-house and shown to bind specifically to HER3 receptors) (Aliquot and store at -20 °C)
  19. Alexa FluorTM 488 NHS Ester (Thermo Fisher Scientific, InvitrogenTM, catalog number: A20100) and Maleimide (Thermo Fisher Scientific, InvitrogenTM, catalog number: A10254) (Store at -20 °C)
  20. Alexa FluorTM 647 NHS Ester (Thermo Fisher Scientific, InvitrogenTM, catalog number: A20006) and Maleimide (Thermo Fisher Scientific, InvitrogenTM, catalog number: A20347) (Store at -20 °C)
  21. Dulbecco's Phosphate-Buffered Saline (DPBS) (Thermo Fisher Scientific, GibcoTM, catalog number: 14040091)
  22. Paraformaldehyde (PFA) (Electron Microscopy Sciences, catalog number: 157-4-100)
  23. Glutaraldehyde (Sigma-Aldrich, catalog number: G5882)
  24. Cysteamine hydrochloride (Sigma-Aldrich, catalog number: M6500)
  25. Zeiss ImmersolTM 518F Imaging Oil 23 °C (Thermo Fisher Scientific, CarlZeissTM ImmersolTM)
  26. Standard tool for calibration MultiSpecTM bead sample (ZEISS, catalog number: 2076-515)
  27. Sodium chloride (Sigma-Aldrich, catalog number: S7653)
  28. Potassium Chloride (Fluka, catalog number: 60128)
  29. Sodium phosphate dibasic (Na2HPO4, Sigma-Aldrich, catalog number: S9763)
  30. Potassium phosphate monobasic (KH2PO4, Sigma, catalog number: P5655)
  31. Tris Base (Sigma, BioUltra, catalog number: 93286)
  32. HEPES solution (Sigma, catalog number: H3537)
  33. DMSO (Invitrogen, catalog number: D12345)
  34. DMEM (Gibco, catalog number: 11880-028)
  35. DTT (Fisher, catalog number: D/P 351/43)
  36. Protease inhibitor cocktail (Cell Signaling Technologies, catalog number: 5871S)
  37. Phosphatase inhibitors (Sodium Fluoride, Sigma, catalog number: S7920 and Sodium Orthovanadate, Sigma, catalog number: S6508)
  38. Phosphate buffered saline (PBS) (Thermo Fisher Scientific, GibcoTM, catalog number: 14040091 or see Recipes)
  39. MEA buffer (see Recipes)

Equipment

  1. Electronic pipette filler/dispenser (Hirschmann®, Pipetus®, model: 9907200)
  2. Nanodrop Spectrophotometer (Thermo Fisher Scientific, model: ND-2000C)
  3. Classic prestige medical Autoclave (Wolf labs, model: 210048)
  4. ZEISS Elyra PS.1 super-resolution microscope (ZEISS, model: ELYRA PS.1) with 1.46 NA 100x oil immersion objective (ZEISS Alpha Plan-Apochromat 100x/1.46 Oil DIC, catalog number: 420792-9800-000)
  5. Fume Hood 
  6. 37 °C, 5% CO2 New BrunswickTM Incubator (Eppendorf, model: Galaxy® 170R, catalog number: CO17301002)
  7. SCANLAF Microbiological Safety Cabinet (Labogene, Mars Safety Class 2)
  8. -4 °C Fridge (LEC medical, model: LR307C, SKU number: UK444441800)
  9. -20 °C Freezer (LEC medical, model: ISU37C, SKU number: UK444441803)
  10. -80 °C Freezer (RS Biotech, model: Eclipse 100v, catalog number: 05100v-230)
  11. Centrifuge (Eppendorf, model: 5702)

Software

  1. ZEN Black 2012 SP5 FP1 (64 bit) software, version 14.0.12.201 (Zeiss)
    Minimum System Requirements: Intel® Xeon X5650 6-Core 2.66 GHz, Intel® 5520 Dual chipset, 6 GB DDR3-RAM, Graphics interface PCIe x16, Graphics adapter ATI FirePro 2560 x 1600 resolution, 32-bit true color, 512 MB RAM, DirectX 8.0 or higher, Monitor 20" TFT 1600 x 1200, Hard disk 1 x 250 GB SATA2 (configured as 250 GB hard drive) and 4 x 1 TB SATA2 (configured as 2 TB RAID 10 hard drive), DVD-ROM drive, 1x free PCI Express Generation 2.0 x16 (mechanical x16, electrical x16) full height slot, Trigger-Board and Signal Distribution Box, 2 x FireWire IEEE 1394a interface, 4 x serial interface (COM1-COM4), 4 x USB interfaces, Microsoft® Windows® 7 Ultimate SP1 x64 (Multilanguage) (no special customer adapted versions of the latter 3 items), The user must be logged on as a member of the local "User" group, For the software installation procedure local administrator rights are required, The hardware requirements of modules can be higher. (taken from https://www.zeiss.com/content/dam/Microscopy/Downloads/Pdf/FAQs/zen2012-sp2_installation_guide.pdf)
  2. MATLAB and ClusDoC package (https://github.com/PRNicovich/ClusDoC)
  3. Custom software (https://github.com/fbi-octopus/cluster_analysis)
    The repository contains:
    1. The ROI selection tool (in python)
    2. The modified cluster analysis software (in R)
    3. A template of LSF script for the use of computing clusters
    4. An example data set (input for the ROI selection tool)
    5. Example data from ROI ready to be clustered
  4. R (R Foundation for Statistical Computing, version 3.2.3 or higher)
    R libraries 'splancs' and 'igraph'
  5. Python 2.7
    Modules 'shapely', 'PyQt4', 'matplotlib', 'PIL' or 'pillow'
  6. ImageJ (https://imagej.net/Downloads)

Procedure

  1. Growing cells
    1. Seed 2.0 x 105 SKBR3 cells onto sterile BSA-coated glass-bottomed dishes and cover with 2 ml of RPMI 1640 media supplemented with 10% FBS and 2 nM L-Glutamine. BSA-coating minimizes non-specific binding of the fluorescent ligands (Zanetti-Domingues et al., 2012).
    2. Incubate cells with 5% CO2 in air at 37 °C for two days or until they have reached ~70% confluency.

  2. Drug treatment and labeling
    1. Conjugate HER2 and HER3 Affibody ligands to the appropriate Alexa Fluor maleimide dyes according to manufacturer’s instructions. Thanks to the unique cysteine site at the C-terminus of Affibody molecules, the dye molecule:ligand molecule can be at most 1:1, but check for incomplete labeling in a spectrophotometer. Briefly:
      1. Dissolve the protein at 50-100 µM in a suitable buffer at pH 7.0-7.5 (10-100 mM phosphate, Tris, HEPES) at room temperature. 
      2. Prepare a 1-10 mM stock solution of the reactive dye in DMSO immediately before use. Protect all stock solutions from light as much as possible by wrapping containers in aluminum foil. 
      3. Add sufficient protein-modification reagent from a stock solution to give approximately 10-20 moles of reagent for each mole of protein. Add the reagent dropwise to the protein solution as it is stirring.
      4. Allow the reaction to proceed for 2 h at room temperature or overnight at 4 °C. 
      5. Separate the conjugate on a gel filtration column, such as a PD-10 purification column. Also see Yan, 2011.

      The degree of labeling can be calculated using the following formula: 



      where Ax = the absorbance value of the dye at the maximum absorption wavelength. ε = molar extinction coefficient of the dye or reagent at the maximum absorption wavelength. For Alexa 488 this is 73,000 cm-1 M-1. The MW (molecular weight) of affibodies is 6,000 Da. Therefore, assuming a 1 mg/ml protein solution has A280 = 1, their molar extinction coefficients are 6,000 cm-1 M-1
    2. Conjugate NRGβ1 to the appropriate NHS ester Alexa Fluor dye according to manufacturer’s instructions and see above. Check dye molecule:ligand molecule ratio in a spectrophotometer and ensure that it is as close to 1:1 as possible (Protein concentration measured at 280 nm and Alexa 647 concentration measured at 647 nm. The extinction coefficient of Alexa 647, at the wavelength of maximum emission (647 nm), is 270,000 cm-1 M-1).
    3. Ensure that the NRG-dye conjugate is as active as the unlabeled protein by performing control Western Blot or ELISA assays for HER3 and HER4 phosphorylation. Briefly for cell culture and Western Blotting: 
      1. Seed MCF7 cells at 0.5 x 105 cells/well in 24-well plates using DMEM supplemented with 10% FBS as growth media. 
      2. Using FuGENE HD, transfect cells with HER3 or HER4 plasmid DNA (follow the manufacturer’s instructions). 
      3. Twenty-four hours post-transfection, treat pre-chilled cells with ice-cold 10 nM NRG-Alexa 647 in PBS and incubate for 1 h at 4 °C. 
      4. Lyse cells in 1x sample buffer containing 1 mM DTT, protease and phosphatase inhibitors. Sonicate and centrifuge before running on SDS-PAGE gel (using XCell apparatus from Invitrogen) alongside HiMark Prestained HMW and Novex Sharp Prestained protein standards (Invitrogen). 
      5. Transfer to a membrane and analyze by Western blotting. Briefly, first bind anti-HER2 pY877 or anti-HER3 pY1289 from Cell Signaling Technology (follow the manufacturer’s instructions) and then the species-specific secondary antibody conjugated to HRP (Horse-radish peroxidase) from Jackson ImmunoResearch. 
      6. Develop the blot using the Supersignal West Pico Chemiluminescent Substrate solution from Pierce. Image with a BioRad ChemiDoc MP system Imager and analyze using densitometry in ImageJ. 
      7. After stripping the blot of antibodies, reprobe with anti-Total HER2 or anti-Total HER3, antibodies and then the species-specific secondary antibody conjugated to HRP. Develop and analyze as above.
    4. Change the media on the cells to serum-free RPMI 1640 media (SFM) including, if required, 14 nM Lapatinib or 41 nM Bosutinib. 
    5. Incubate all dishes at 37 °C/5% CO2 for two hours.
    6. Using a serological pipette and electronic pipette filler/dispenser, remove the cell media and replace with 1 ml PBS to wash. Remove the PBS to add 1 ml of fresh PBS including the appropriate drug treatment if required. 
    7. Chill all samples on ice at 4 °C for 10 min (to minimize receptor internalization).
    8. Incubate half of the samples in 1 ml of 100 nM HER2Affibody-Alexa488 and 50 nM HER3Affibody-Alexa647 diluted in ice-cold PBS and, if required, the appropriate drug (either 14 nM Lapatinib or 41 nM Bosutinib).
    9. Incubate the remaining dishes in 1 ml of 100 nM HER2Affibody-Alexa488 and 10 nM NRG-Alexa647 diluted in ice-cold PBS and, if required, the appropriate drug. 
    10. Incubate all the dishes on ice for at least 1 h. 
    11. Wash three times each with 1 ml of ice-cold PBS.
    12. Fix cells with 1 ml of 4% paraformaldehyde and 0.5% glutaraldehyde diluted into ice-cold PBS. 
    13. To ensure complete fixation, incubate cells on ice at 4 °C for 30 min-1 h, then wash three times using 1 ml of PBS per wash.
    14. Store samples at 4 °C in PBS and prior to imaging supplement fresh PBS with 50 mM cysteamine HCl (total volume 1 ml).

  3. Imaging
    1. Supplement objective lens (1.46 NA 100x oil immersion objective) with immersion oil and mount sample on microscope stage (Figure 1 and see
      https://applications.zeiss.com/C125792900358A3F/0/8DF1FE51A0C52599C1257C1D0073F96D/$FILE/EN_41_011_061_ELYRA.pdf for microscope setup).
    2. Locate sample using eyepiece and set up a 2 dimensional (2D), 2 color dSTORM (Endesfelder and Heilemann, 2015) experiment using the 488 nm and 641 nm excitation laser lines in separate tracks with the appropriate filters (a possible setup for this pair of dyes would be BP420-480/BP495-560/LP650). See Figure 2 (left) for parameter settings in Zen software.
    3. Locate a cell and position it in the center of the field of view (FOV) then refocus, ensuring that the entire cell membrane is clearly in focus (Figure 2 [right] for example image). 
    4. Using TIRF illumination, gradually increase laser powers separately for each channel to excite the Alexa 488 and Alexa 647 fluorophores and push them towards quasi-equilibrium (Dempsey et al., 2011).
    5. Acquire dSTORM raw data over 10,000 frames with exposure time 20 ms, EM gain 250, alternating the lasers to image the two receptors sequentially every 300 frames.
    6. Use the 405 nm laser line, if necessary, at low power (< 1%) to aid fluorophore blinking at any time. 
    7. During acquisition use excitation laser powers of ~9-10 kW/cm2 at the sample (we used 7.6-11.7 kW/cm2, average 8.9 kW/cm2 for 488 nm laser and 7.6 -13.8 kW/cm2, average 10.4 kW/cm2 for 640 nm laser). Low levels of 405 nm laser can also be used (0.001-0.005 kW/cm2).
    8. Repeat imaging procedure to obtain dSTORM data for at least 12 regions (25.6 µm x 25.6 µm) per treatment group with each region including at least one cell.


      Figure 1. STORM imaging set-up. 1. Microscope: Axio Observer, Z1 (inverse stand); Incubator XL dark; Motorized Piezo XY scanning stage; Z-Piezo stage insert; Port for LSM attachment; two camera ports. 2. Objectives: Plan-APOCHROMAT 100x/1.46 Oil (DIC). 3. ELYRA Illumination and Detection: Fiber coupled solid state and diode pumped solid state lasers; 405 nm diode (50 mW); 488 nm OPSL (100 mW); 561 nm OPSL (100 mW); 642 nm diode (150 mW); Andor iXon 897 EM-CCD camera. 4. Software: ZEN (black edition), PALM module.


      Figure 2. Zen settings for acquisition parameters (left) and Example image (right). Left: 1. Excitation lasers and power; 2. Tracks; 3. Emission filter; 4. TIRF setting and angle; 5. Number of frames; 6. Exposure time; 7. EM gain. 8. Frames per cycle.

    9. Use the PALM function in ZEN software to process and render the images (Figure 3). Use ZEN to first define the point spread function (PSF) mask size (typically 9 pixels, max 10 pixels) and intensity to noise ratio (typically 6) then apply to localize each blinking event using a 2D Gaussian fit model. 
    10. Take overlapping fluorophores into account using the Account for overlap setting. 
    11. Correct for displacements of molecules in the lateral plane from drifts using feature detection and cross-correlation methods in the PAL-Drift tab.
    12. Implement channel alignment (Figure 4) of the reconstructed images using a standard MultiSpec bead sample.
    13. Co-ordinates of the localizations in the final reconstructed images (typically 30,000+ for HER2 and 5,000+ for HER3 per region, Figure 3) are passed into the Bayesian cluster identification algorithm designed by Dylan Owen et al. (Griffié et al., 2016).


      Figure 3. Zen settings for data analysis parameters. 1. PALM function. 2. PSF mask size. 3. Intensity to noise ratio. 4. Fit model. 5. Account for overlap. 6. PAL-drift tab. 7. Co-ordinates of localizations and precision.


      Figure 4. Channel alignment settings

Data analysis

  1. Analysis using Bayesian Cluster Algorithm
    A Bayesian cluster algorithm is applied to determine the cluster radii and number of molecules in the cluster (Griffié et al., 2016). The analysis can be done under Linux or Windows OS, although the region-of-interest (ROI) selection tool has not been tested for Windows. The software requires python 2.7 and R. Both packages, including all required libraries and modules, are free and open source and available for both Linux and Windows OS. The installation depends on your specific OS. On Ubuntu 16.04, all required software can be installed using the “apt install” command, except for the R library ‘splancs’, which can be installed by running R and typing ‘install.packages(“splancs”)’, The ‘igraph’ library could be installed in this manner as well. All required python modules can alternatively be installed using python’s ‘pip’ tool. Run ‘pyrcc4 resources.qrc –o qrc_resources.py’ in the roi_selector folder before you run ‘roi_select.py’ the first time. Otherwise, the tool buttons will have text labels instead of icons (Video 1).
    Once the required software is set up, the analysis consists of the following principal steps.

    Table 1. Schematic of the data analysis using the Bayesian Cluster Algorithm


    The data from different channels of an acquisition is stored in folders that have the same base name plus an extension “green” or “red” for the channel (such as “s07a06green” and “s07a06red” for a data set “s07a06”, see also Video 1), folder names need to end in either “green” or “red” (note this is a critical step) This is done to aid the ROI selection (note, the tool works for 1 or 2 channels). Otherwise, the conventions of the protocol (Griffié et al., 2016) are followed. The clustering algorithm expects the background and clusters to be uniformly distributed over a rectangular ROI. The analyzed images mainly show single cells. Labeled molecules of interest are in the cell membrane. The cell membrane is visible as a roughly circular shape. In order to conform to the prerequisites of the clustering algorithms, rectangular regions need to be manually or semi-automatically selected.

    Video 1. Preparation. What the input data looks like, what the ROI selector looks like without icons, how to compile the python resources for the ROI selector, and what the ROI selector looks like with icons.

    This is accomplished with a graphical tool written in Python (provided). The tool displays the data of all channels of an acquisition, each channel in a different color. Ensure that the rectangles tightly cover the cell membrane using the most suitable angles. The semi-automatic procedure can be used to optimize a manually selected ROI (which does not need to be rectangular or of tight fit around the membrane) with a single button, -click. The whole cell membrane is covered in this way by ROIs. The data selected by these regions is rotated automatically so that the sides of the rectangles becomes parallel to the coordinate axis and the selected data is written to files, one folder for each ROI. The following workflow is used (Figure 5, Video 2):
    1. Open the data file, key ‘ctrl-L’ or button (1).
    2. Select a drawing mode, button (4), if desired. 
    3. Start drawing, key ‘d’ or button (3).
    4. Draw the rectangle or polygon by clicking on the image
    5. The rectangle will be complete after 3 points have been selected (two points for the base line and the third to determine the height), the polygon will be complete after clicking button (3) or pressing ‘d’. The first and the last point will be connected to close the polygon.
    6. Optimize the ROI, button (5) or key ‘s’, this will turn the highlighted ROI into a rectangle that contains the most dense regions of the highlighted ROI.
    7. Repeat 3-6.
    8. Save the rectangular ROI (it is critical to note that the polygons will not be saved), button (2) or key ‘Ctrl + S’. The first time you do this in each session, you will be asked to select the output folder. The data will be stored in sub-folders of the selected folder. The output folder also can be set from the menu: “File” → “Set base output directory”.
    9. Go to 1.


    Figure 5. Features of the region of interest selection tool. Open data set (1), Save rectangular ROI (2), Start ROI drawing (3), Select ROI type: polygon or rectangle (4), automatically optimize selected ROI (5), ROI’s (6), selected ROI (7), options toolbar (8), Python’s matplotlib toolbar (9), editable message window (10).

    Video 2. Usage of the ROI selection tool. How to open a data set, how to activate the scatter plot display mode, how to zoom and pan, introduction to the main toolbar elements, how to use the polygon selection tool, how to use the optimize-ROI tool, how to save the selected data and the contents of the resulting output folders.

    The drawing mode can be changed any time, ROI can be removed and information about the ROI (number of observations, density) can be displayed. The tool will write the required configuration-file too. The results are a series of folders such as (for Linux OS):
    1) <base-output-directory>/s07a06r23415-14057_1466x3564_25red
    2) <base-output-directory>/s07a06r23415-14057_1466x3564_25green
    3) <base-output-directory>/s07a06r18906-21057_1101x2745_76red
    4) <base-output-directory>/s07a06r18906-21057_1101x2745_76green
    5) …
    where each folder contains two files:
    1) config.txt
    2) 1/data.txt
    Note: A channel may not contain any data in a particular ROI. In such a case no output is written for the channel. Note, the channels are processed independently from each other.
      The files are used as input for the clustering algorithm and the algorithm was applied as described by the protocol (Griffié et al., 2016) (Video 3). The original source files (supplementary information in Griffié et al., 2016) have been modified (provided) to promote batch analysis. The analysis is executed by running:

    R -f run.R --args --target=<data-folder> --config==<path-config-file>

    From the folder that contains the ‘run.R’ script. Here, <data-folder> is the folder that contains data from the ROI (such as ‘<base-output-directory>/s07a06r18906-21057_1101x2745_76red’) and <path-to-config-file> is the path to the ‘config.txt’ belonging to the data (here ‘<base-output-directory>/s07a06r18906-21057_1101x2745_76red/config.txt’). Since the number of folders can be large, computing clusters may be used. We provide an example of how to run the scripts on clusters with the LSF job scheduler.

    After the analysis with ‘run.R’ has completed, <data-folder> contains:
    1) config.txt
    2) 1/data.txt
    3) 1/all_labels.txt.gz
    4) 1/r_vs_thresh.txt
    The files are processed with the ‘postprocessing.R’ script (Video 3):

    R --slave -f postprocessing.R --args –folders=<data-folder>

    where again <data-folder> is the folder that contains data from the ROI. This script adds three files to <data-folder> :
    1) 1/summary.txt
    2) radii.txt
    3) nmols.txt
    where ‘radii.txt’ contains the list of cluster sizes and ‘nmols.txt’ contains the list of molecules per cluster, which are the desired results.

    Video 3. Running the clustering algorithm on the command line. How to use the information from the README file, how to edit the command line, how to run the primary clustering algorithm, the result of the algorithm, how to run the post processing, result of the post processing.

  2. Analysis using Clus DoC
    1. For further image analysis to compare the degree of colocalization between HER2 and HER3, with the different treatments, dSTORM images were analyzed using Clus-DoC (Cluster detection with Degree of Colocalization) program run in the MATLAB environment (Pageon et al., 2016). A schematic of this data analysis process can be found in Figure 3 of Pageon et al. (2016).
    2. The graphical user interface (GUI) was opened in MATLAB using the command line ‘ClusDoC’ (Figure 6), and the previously saved table of localizations from ZEISS ZEN software (in text file format) for each image was loaded as the ‘Input File’ (A).
    3. The destination for the data to be produced was then selected using the ‘Set Output Path’ button (B). 
    4. Membranes of entire cells were selected by drawing around the membrane using the ‘Add ROI’ tool (C) and then double-clicking to activate. 
    5. The ROI was saved by clicking the button ‘Export ROIs’ (D).
    6. A Ripley K test (E) was performed first to obtain a maximum L(r)-r value for each cell (shown on the ‘RipleyK Plots’ produced). This value was input as the L(r)-r parameter for subsequent DBSCAN and Clus-DoC tests, to filter out noise points. 
    7. Multiple DBSCAN tests (F) were then run on each cell to determine the parameters required to obtain an average cluster area that agreed with that predicted by the Bayesian cluster identification algorithm. 
    8. The final parameters were recorded then input into the Clus-DoC test parameters, and a Clus-DoC test (G) was run on each cell to quantify the degree of colocalization between HER2 and HER3.


      Figure 6. ClusDoC GUI with table of localizations loaded and ROI selected. Buttons of the GUI are linked to the relevant steps in the analysis procedure by labels A-G.

    9. This procedure was repeated for every image.
    10. Properties such as cluster size and colocalization percentage (see Pageon et al., 2016; Table 1 for complete list) in the Excel result tables produced (in the specified output path folder) were then compared between treatment groups.
    11. Results can be found in Claus et al. (2018).

Recipes

  1. Phosphate buffered saline (PBS)
    137 mM NaCl
    2.7 mM KCl
    10 mM Na2HPO4
    1.8 mM KH2PO4
    Adjust pH to 7.4 and autoclave to sterilize (121 °C, 15 min)
    Store PBS at 4 °C
  2. MEA buffer
    50 mM Cysteamine hydrochloride (diluted from 100 mM stock stored at -20 °C) in PBS
    Use the diluted 50 mM working solution immediately

Acknowledgments

This work has been funded by MRC grant (Ref. MR/K015591/1) from the Medical Research Council and by BBSRC grant BB/G006911/1 from the Biotechnology and Biological Sciences Research Council. This work was also supported in part by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC0010130), the UK Medical Research Council (FC0010130), and the Wellcome Trust (FC0010130).

Competing interests

Authors declare no conflicts of interest or competing interests.

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简介

人表皮生长因子受体(HER)受体酪氨酸激酶家族由四个单通道跨膜受体同源物(HER1-4)组成,其作用是调节正常细胞和肿瘤细胞中的许多关键过程。 HER2在许多肿瘤中过表达,并且通过与其优选的受体配偶体HER3的合作驱动癌细胞的失调的增殖。使用超分辨率显微镜评估标记的HER2和HER3的原位>组织,揭示了定量单分子定位显微镜(SMLM)作为表征受体簇的理想生物分析工具。受体聚类是引导细胞响应刺激的重要调节机制,因此,为了理解这些过程,有必要测量参数,例如簇的数量,簇半径和每簇的局部化数量以用于不同的扰动。以前,具有光漂白的荧光定位成像(FLImP)是另一种纳米级单分子技术,其特征在于细胞膜中HER1 [或表皮生长因子受体(EGFR)]的寡聚化状态。为了使用FLImP实现EGFR寡聚体中分子间分离的前所未有的分辨率(<5nm),很少有受体被标记,因此该方法不适用于测量受体经常被上调的癌细胞中的整个受体群体。在这里,为了检测参与簇形成的所有受体,在存在或不存在治疗药物(拉帕替尼或博舒替尼)的情况下,我们用1:1染料与蛋白质比率的配体使内源性HER2和HER3膜受体饱和。这在常用的乳腺癌细胞系SKBR3细胞中进行,其中有约160万HER2受体/细胞和10,000-40,000个HER3受体/细胞。使用HER2-或HER3-特异性Affibody研究这些受体的基础状态,同样,使用天然HER3配体Neuregulin-beta1(NRGβ1)探测活性状态。随机光学重建显微镜(STORM),一种形式的SMLM,在此用于成像细胞,其被化学固定以最小化图像模糊并提供用于聚类分析的数据(x和y坐标以及测量的局部化的标准偏差)。进一步分析还可以确定受体共定位的比例。我们的研究结果表明,拉帕替尼结合的HER2通过非经典激酶二聚体结构与HER3复合,诱导更高级的寡聚体。我们假设受体的成核产生信号平台,其解释了在HER2抑制剂拉帕替尼存在下,在配体结合时细胞增殖的反直觉增加。

【背景】同源和异源蛋白聚类促进细胞中信号传导过程的有效调节(Sourjik和Armitage,2010; Nussinov,2013; Truong-Quang和Lenne,2014; Recouvreux和Lenne,2016)。机制包括但不限于蛋白质的隔离(例如>,GPI-锚定的叶酸受体[Mayor et al。>,1994]),对接位点的产生( 例如>,Syntaxin / SNARE家族蛋白[Sieber et al。>,2007]),相互作用的空间位阻(例如>,Rictor和mSin1在mTOR中complex-2 [Chen et al。>,2018]),错误折叠蛋白的聚集(例如>,淀粉样蛋白原纤维[Adamcik et al。>,2010] ]),趋化期间对平衡前信号的加速反应(Ventura et al。>,2014),局灶性粘连中整合素的调节(Spiess et al。>,2018)信号级联的形成(例如>,GPCR信号传导[Maurel et al。>,2008; Jonas et al。>,2015])。具体而言,我们专注于膜受体聚类/寡聚化的分析,以研究由于药物结合引起的分子重排。我们强调了相互合作的重要性,从而增加了信号输出(Claus et al。>,2018),这是一个可能在信号级联中常见的主题。此外,我们建议该方案通常适用于许多与蛋白质簇有关的生物学问题。对样品制备的微小修改应允许对任何蛋白质进行定位测量,所述蛋白质固定在玻璃上或在细菌或其他(例如>,哺乳动物)细胞中表达。蛋白质相互作用的模型和考虑野生型蛋白质和包含相关突变的等同物之间的差异通常可以导致更全面的分子机制。因此,我们相信该协议提供的定量聚类和共定位数据可进一步提高我们对本地和疾病状态的理解。

本研究中感兴趣的蛋白质HER2和HER3是HER受体酪氨酸激酶家族的两个成员(Normanno et al。>,2006; Eccles,2011)。 HER2在许多肿瘤中过度表达,并且通过与其优选的受体配偶体HER3的合作来驱动癌细胞的失调的增殖(Holbro 等,>,2003)。 SMLM技术(例如>,STORM用于可光活化的荧光团或PALM用于遗传表达的标记蛋白质)可以在质膜或细胞内成像单个荧光标记的蛋白质(如受体)(Nicovich et al。>,2017)。由于相对简单的实验设置和程序(Rust et al。>,2006)以及用于处理,分析和可视化数据的免费开源软件的可用性,STORM是一种流行的成像模式( Ovesný et al。>,2014)。我们选择STORM用于我们的应用,因为荧光团闪烁是随机诱导的,并且定位一次实现一组荧光团,该过程继续并且信号累积直到建立详细图像。至关重要的是,STORM过程使得该技术与所有内源性HER2蛋白的饱和标记相容,使用Affibody配体,该配体是小的,特异性的并且以1:1 Affibody:染料的比例与Alexa 488均匀缀合(Wikman et al。 >,2004; Eigenbrot et al。>,2010)。其他HER蛋白亲和体可用(Friedman et al。>,2007; Nordberg et al。>,2007; Kronqvist et al。>,2010; Gostring et al。>,2012;)。反过来,SMLM数据的聚类分析可以揭示每个聚类的分子数和聚类大小。我们使用的后期分析软件可以从Dylan Owen(Griffié et al。>,2016)和Katharina Gaus(Pageon et al。>,2016)领导的研究团队中获得。结合此处提供的自定义书面工具。如果需要,可以使用SMLM和聚类分析来研究活细胞中的这些受体(分析方法的扩展在Griffié et al。>,2018中给出)但是这还没有实施到目前为止。

我们还想描述簇内蛋白质 - 蛋白质相互作用的特征,这些信息不能仅通过聚类分析获得。使用荧光寿命成像显微镜(FRET-FLIM)的荧光共振能量转移测量也广泛用于Claus 等人>以测量HER2和HER3及其各自突变体之间抑制剂诱导的异型相互作用(Claus 等人>,2018)。 FRET-FLIM数据通过报告异二聚体复合物的稳定性来补充簇的分析。然而,由于FRET的长度尺度为2-8nm,因此该方法不能用于报告通常较大的簇尺寸,该组合对于提供这些受体的更深层次组织视图是重要的。

其他用于测量蛋白质 - 蛋白质相互作用和聚类的成像技术可以与聚类分析结合使用,以提供补充信息,并从动态和结构的角度提供更完整的基础过程图。其中包括双色单粒子追踪(SPT),它报告了成对受体粒子相互作用的数量和这些相互作用的持续时间(Low-Nam et al。>,2011; Needham et al 。>,2013)允许区分多聚体和二聚体。然而,SPT在活细胞上移动时对单个分子或颗粒进行成像,相关的模糊意味着SPT通常没有足够的分辨率来确定移动时成像的“粒子”中的受体数量。 FLImP是测量静态粒子上精确受体分离的理想方法(Needham et al。>,2013)。在模型细胞系统中,可以优化受体的表达水平。然而,HER2在SKBR3细胞系中高度过表达(Eccles,2011; Shankaran et al。>,2013),并且标记需要保持在低水平。然而,FLImP应该能够提供与FRET在受体 - 受体相互作用和复合物几何形状上互补的信息。受激发射消耗(STED)显微镜是一种独特但互补的STORM超分辨率成像技术,可以测量直径约50 nm的蛋白质簇。利用该分辨率对固定的,免疫标记的细胞样品,Kellner 等>首次测量了烟碱乙酰胆碱受体(AChR)纳米团簇的大小分布变化(Kellner et al。 >,2007)和Dzyubenko最近采用这种方法揭示突触后神经元中标记蛋白的密度(Dzyubenko et al。>,2016)。从这两种技术得到的活性整联蛋白的最近邻距离的直接比较表明,STORM比STED具有更高的分辨率(Spiess et al。>,2018)。然而,后一种技术可以将聚类分析扩展到活细胞并实现更高的时间分辨率。如果可以进行稳健的荧光团校准,则数量和亮度分析将提供来自活细胞的STED数据的化学计量和动态测量。

关键字:表皮生长因子受体, 人表皮生长因子受体2, 人表皮生长因子受体3, 单分子定位显微术, 随机光学重建显微法, 拉帕替尼, 聚类分析

材料和试剂

  1. 75 cm 2 过滤器盖帽细胞培养瓶(Thermo Fisher Scientific,Nunc TM EasYFlask TM ,目录号:156499)
  2. 35 mm玻璃底盘,高公差1.5盖板,14 mm玻璃直径(MatTek Corporation,目录号:P35G-0.170-14-C)&nbsp;
  3. 5 ml单独包装的无菌血清移液器(Corning合并,Costar Stripette TM ,目录号:4487)
  4. 10 ml单独包装的无菌血清移液器(Corning合并,Costar Stripette TM ,目录号:4488)
  5. 25 ml单独包装的无菌血清移液器(Corning合并,Costar Stripette TM ,目录号:4489)
  6. PD10纯化柱(GE Healthcare,目录号17085101)
  7. 24孔板(Nunclon TM ,目录号:143982)
  8. 铝箔(Terinex,目录号:11330)
  9. SK-BR-3 [SKBR3](ATCC ® HTB-30 TM )细胞(LGC标准品)(ATCC,目录号:HTB-30)
  10. 牛血清白蛋白(BSA)(西格玛奥德里奇,目录号:A1470)(在4°C下储存)
  11. 不含酚红的RPMI 1640培养基(Thermo Fisher Scientific,Gibco TM ,目录号:11835063)(储存于4°C)
  12. 胎牛血清(FBS)(Thermo Fisher Scientific,Gibco TM ,目录号:10270)(等分试样并在-20°C储存)
  13. L-谷氨酰胺(Thermo Fisher Scientific,Gibco TM ,目录号:25030024)(等分试样并在-20°C储存)
  14. 拉帕替尼(BioVision,目录号:1624)(分装并储存于-20°C)
  15. Bosutinib(Sigma-Aldrich,目录号:PZ0192)(分装并储存于-20°C)
  16. Neuregulin beta-1(NRGβ1)(Peprotech,目录号:100-03)(等分试样并在-20°C保存)
  17. 抗HER2,显像剂(Affibody Incorporated,目录号:10.1861.01.0005)(将粉末储存在4°C,一次重构,等分并储存在-20°C)
  18. HER3 Affibody配体(质粒是来自JohnLöfblom的礼物[Kronqvist et al。>,2011],一种在内部表达和纯化的蛋白质,并显示与HER3受体特异性结合)(等分并储存于 - 20°C)
  19. Alexa Fluor TM 488 NHS酯(Thermo Fisher Scientific,Invitrogen TM ,目录号:A20100)和马来酰亚胺(Thermo Fisher Scientific,Invitrogen TM ,目录号:A10254)(-20°C以下的储存)
  20. Alexa Fluor TM 647 NHS酯(Thermo Fisher Scientific,Invitrogen TM ,目录号:A20006)和马来酰亚胺(Thermo Fisher Scientific,Invitrogen TM ,目录号:A20347)(-20°C以下的储存)
  21. Dulbecco的磷酸盐缓冲盐水(DPBS)(Thermo Fisher Scientific,Gibco TM ,目录号:14040091)
  22. 多聚甲醛(PFA)(Electron Microscopy Sciences,目录号:157-4-100)
  23. 戊二醛(Sigma-Aldrich,目录号:G5882)
  24. 半胱胺盐酸盐(Sigma-Aldrich,目录号:M6500)
  25. Zeiss Immersol TM 518F成像油23°C(Thermo Fisher Scientific,CarlZeiss TM Immersol TM )
  26. 用于校准的标准工具MultiSpec TM 珠子样品(蔡司,目录:2076-515)
  27. 氯化钠(Sigma Aldrich,目录号:S7653)
  28. 氯化钾(Fluka,目录号:60128)
  29. 磷酸氢二钠(Na 2 HPO 4 ,Sigma Aldrich,目录号:S9763)
  30. 磷酸二氢钾(KH 2 PO 4 ,Sigma,目录号:P5655)
  31. Tris Base(Sigma,BioUltra,目录号:93286)
  32. HEPES解决方案(Sigma,目录号:H3537)
  33. DMSO(Invitrogen,目录号:D12345)
  34. DMEM(Gibco,目录号:11880-028)
  35. DTT(Fisher,目录号:D / P 351/43)
  36. 蛋白酶抑制剂鸡尾酒(Cell Signaling Technologies,目录号:5871S)
  37. 磷酸酶抑制剂(氟化钠,Sigma,目录号:S7920和Sodium Orthovanadate,Sigma,目录号:S6508)
  38. 磷酸盐缓冲盐水(PBS)(Thermo Fisher Scientific,Gibco TM ,目录号:14040091或参见食谱)
  39. MEA缓冲液(见食谱)

设备

  1. 电子移液器/分配器(Hirschmann ®,Pipetus ®,型号:9907200)
  2. Nanodrop分光光度计(Thermo Fisher Scientific,型号:ND-2000C)
  3. 经典声望医疗高压灭菌器(Wolf labs,型号:210048)
  4. 蔡司Elyra PS.1超分辨率显微镜(蔡司,型号:ELYRA PS.1),带1.46 NA 100x油浸物镜(ZEISS Alpha Plan-Apochromat 100x / 1.46 Oil DIC,目录号420792-9800-000)
  5. 通风柜&nbsp;
  6. 37°C,5%CO 2 新不伦瑞克 TM 培养箱(Eppendorf,型号:Galaxy ® 170R,目录号:CO17301002)
  7. SCANLAF微生物安全柜(Labogene,Mars Safety Class 2)
  8. -4°C冰箱(LEC医疗,型号:LR307C,SKU编号:UK444441800)
  9. -20°C冰箱(LEC医疗,型号:ISU37C,SKU编号:UK444441803)
  10. -80°C冰箱(RS Biotech,型号:Eclipse 100v,目录号:05100v-230)
  11. 离心机(Eppendorf,型号:5702)

软件

  1. ZEN Black 2012 SP5 FP1(64位)软件,版本14.0.12.201(蔡司)
    最低系统要求:Intel ® Xeon X5650 6核2.66 GHz,Intel ® 5520双芯片组,6 GB DDR3-RAM,图形接口PCIe x16,图形适配器ATI FirePro 2560 x 1600分辨率,32位真彩色,512 MB RAM,DirectX 8.0或更高版本,监视器20“TFT 1600 x 1200,硬盘1 x 250 GB SATA2(配置为250 GB硬盘驱动器)和4 x 1 TB SATA2(已配置)作为2 TB RAID 10硬盘驱动器),DVD-ROM驱动器,1x免费PCI Express Generation 2.0 x16(机械x16,电气x16)全高插槽,触发板和信号分配盒,2 x FireWire IEEE 1394a接口,4 x串行接口(COM1-COM4),4个USB接口,Microsoft ® Windows ® 7 Ultimate SP1 x64(多语言)(后3项没有特殊的客户改编版本),用户必须以本地“用户”组成员的身份登录,对于软件安装过程,需要本地管理员权限,模块的硬件要求可以更高。 rom https://www.zeiss.com/内容/大坝/显微镜/下载/ PDF /常见问题/ zen2012-sp2_installation_guide.pdf )
  2. MATLAB和ClusDoC包( https://github.com/PRNicovich/ClusDoC )
  3. 自定义软件( https://github.com/fbi-octopus/cluster_analysis )
    存储库包含:
    1. ROI选择工具(在python中)
    2. 修改后的集群分析软件(R中)
    3. 用于计算集群的LSF脚本模板
    4. 示例数据集(ROI选择工具的输入)
    5. ROI准备集群的示例数据
  4. R(R统计计算基础,版本3.2.3或更高版本)
    R库'splancs'和'igraph'
  5. Python 2.7
    模块“匀称”,“PyQt4”,“matplotlib”,“PIL”或“枕头”
  6. ImageJ( https://imagej.net/Downloads )

程序

  1. 生长细胞
    1. 种子2.0 x 10 5 SKBR3细胞到无菌BSA涂层玻璃底盘上,盖上
      2ml补充有10%FBS和2nM L-谷氨酰胺的RPMI 1640培养基。 BSA涂层使荧光配体的非特异性结合最小化(Zanetti-Domingues et al。>,2012)。
    2. 将含有5%CO 2 的细胞在空气中于37℃孵育两天或直至它们达到约70%汇合。

  2. 药物治疗和标签
    1. 根据制造商的说明将HER2和HER3 Affibody配体缀合至合适的Alexa Fluor马来酰亚胺染料。由于Affibody分子C末端独特的半胱氨酸位点,染料分子:配体分子可以至多1:1,但检查分光光度计中的不完全标记。简而言之:
      1. 在室温下将蛋白质以50-100μM溶于pH7.0-7.5(10-100mM磷酸盐,Tris,HEPES)的合适缓冲液中。&nbsp;
      2. 使用前立即在DMSO中制备1-10mM活性染料的储备溶液。通过将容器包裹在铝箔中,尽可能地保护所有储备溶液免受光照。&nbsp;
      3. 从储备溶液中加入足够的蛋白质修饰试剂,每摩尔蛋白质得到约10-20摩尔的试剂。在搅拌时将试剂滴加到蛋白质溶液中。
      4. 使反应在室温下进行2小时或在4℃下进行过夜。&nbsp;
      5. 将缀合物分离在凝胶过滤柱上,例如PD-10纯化柱。另见Yan,2011。

      标签的程度可以使用以下公式计算:&nbsp;



      其中Ax =最大吸收波长处染料的吸光度值。 ε=染料或试剂在最大吸收波长下的摩尔消光系数。对于Alexa 488,这是73,000cm -1 M -1 。亲和体的MW(分子量)为6,000Da。因此,假设1mg / ml蛋白质溶液具有A 280 = 1,则它们的摩尔消光系数为6,000cm -1 M -1 。 &NBSP;
    2. 根据制造商的说明将NRGβ1缀合至合适的NHS酯Alexa Fluor染料,并参见上文。在分光光度计中检查染料分子:配体分子比例并确保其尽可能接近1:1(280 nm处测量的蛋白质浓度和647 nm处测量的Alexa 647浓度.Alexa 647的消光系数,波长为最大发射(647nm),为270,000cm -1 M -1 )。
    3. 通过对HER3和HER4磷酸化进行对照Western印迹或ELISA测定,确保NRG-染料缀合物与未标记的蛋白质一样有活性。简要介绍细胞培养和Western Blotting:&nbsp;
      1. 使用补充有10%FBS作为生长培养基的DMEM,在24孔板中以0.5×10 5个细胞/孔接种MCF7细胞。&nbsp;
      2. 使用FuGENE HD,用HER3或HER4质粒DNA转染细胞(遵循制造商的说明)。&nbsp;
      3. 转染后二十四小时,用冰冷的10nM NRG-Alexa 647在PBS中处理预冷的细胞,并在4℃下孵育1小时。
      4. 在含有1mM DTT,蛋白酶和磷酸酶抑制剂的1x样品缓冲液中裂解细胞。在SDS-PAGE凝胶(使用来自Invitrogen的XCell装置)上运行之前进行超声处理和离心,以及HiMark Prestained HMW和Novex Sharp Prestained蛋白质标准品(Invitrogen)。&nbsp;
      5. 转移至膜并通过蛋白质印迹分析。简而言之,首先结合来自Cell Signaling Technology的抗HER2 pY877或抗HER3 pY1289(遵循制造商的说明书),然后结合来自Jackson ImmunoResearch的与HRP(辣根过氧化物酶)缀合的物种特异性二抗。&nbsp;
      6. 使用Pierce的Supersignal West Pico化学发光底物溶液开发印迹。使用BioRad ChemiDoc MP系统成像图像并使用ImageJ中的光密度测定法进行分析。&nbsp;
      7. 在剥离抗体印迹后,用抗总HER2或抗总HER3,抗体再接种,然后与HRP缀合的物种特异性二抗。如上所述进行开发和分析。
    4. 将细胞上的培养基更换为无血清RPMI 1640培养基(SFM),包括(如果需要)
      14 nM拉帕替尼或41 nM Bosutinib。&nbsp;
    5. 将所有培养皿在37°C / 5%CO 2 下孵育2小时。
    6. 使用血清移液管和电子移液器填充器/分配器,取出细胞培养基并用1 ml PBS替换以进行清洗。取出PBS,加入1毫升新鲜PBS,如果需要,包括适当的药物治疗。&nbsp;
    7. 将所有样品在冰上于4℃冷却10分钟(以使受体内化最小化)。
    8. 将一半样品在冰冷的PBS中稀释的1ml 100nM HER2Affibody-Alexa488和50nM HER3Affibody-Alexa647中孵育,并且如果需要,将合适的药物(14nM拉帕替尼或41nM Bosutinib)孵育。
    9. 将剩余的培养皿在1ml 100nM HER2Affibody-Alexa488和10nM NRG-Alexa647中孵育,在冰冷的PBS中稀释,如果需要,将其与适当的药物一起孵育。&nbsp;
    10. 将所有餐具在冰上孵育至少1小时。&nbsp;
    11. 用1ml冰冷的PBS洗涤三次。
    12. 用1ml 4%多聚甲醛和0.5%戊二醛稀释到冰冷的PBS中固定细胞。&nbsp;
    13. 为确保完全固定,将细胞在冰上于4℃孵育30分钟-1小时,然后每次洗涤使用1ml PBS洗涤三次。
    14. 将样品在4℃下储存在PBS中,然后在成像前用50mM半胱胺HCl(总体积1ml)补充新鲜PBS。

  3. 成像
    1. 补充物镜(1.46 NA 100x油浸物镜),浸入油并在显微镜载物台上安装样品(图1,见
      ) https://applications.zeiss.com/C125792900358A3F/0/8DF1FE51A0C52599C1257C1D0073F96D/用于显微镜设置的$ FILE / EN_41_011_061_ELYRA.pdf
    2. 使用目镜定位样品,并使用488 nm和641 nm激发激光线在不同的轨道中使用适当的滤光片设置2维(2D),2色dSTORM(Endesfelder和Heilemann,2015)实验(这对可能的设置BP420-480 / BP495-560 / LP650)染料。有关Zen软件中的参数设置,请参见图2(左)。
    3. 找到一个单元格并将其放置在视野(FOV)的中心,然后重新聚焦,确保整个细胞膜清晰对焦(图2 [右],例如图像)。&nbsp;
    4. 使用TIRF照明,逐渐增加每个通道的激光功率,以激发Alexa 488和Alexa 647荧光团,并将它们推向准平衡(Dempsey et al。>,2011)。
    5. 在曝光时间为20毫秒,EM增益为250的情况下获取超过10,000帧的dSTORM原始数据,交替激光以每300帧顺序成像两个受体。
    6. 如有必要,使用405 nm激光线,低功率(<1%),以帮助荧光团随时闪烁。&nbsp;
    7. 在采集过程中,使用激发激光功率为~9-10 kW / cm 2 样品(我们使用7.6-11.7 kW / cm 2 ,平均8.9 kW / cm 2 对于640nm激光器,平均为10.4kW / cm 2, 2 ,对于640nm激光器,平均为10.4kW / cm 2。也可以使用低水平的405nm激光(0.001-0.005kW / cm 2 2 )。
    8. 重复成像程序以获得每个治疗组至少12个区域(25.6μm×25.6μm)的dSTORM数据,每个区域包括至少一个细胞。


      图1. STORM成像设置。 1。显微镜:Axio Observer,Z1(反向支架);孵化器XL暗;电动压电XY扫描台; Z-Piezo阶段插入; LSM附件端口;两个摄像头端口。 2.目标:Plan-APOCHROMAT 100x / 1.46油(DIC)。 3. ELYRA照明和检测:光纤耦合固态和二极管泵浦固态激光器; 405纳米二极管(50毫瓦); 488nm OPSL(100mW); 561 nm OPSL(100 mW); 642纳米二极管(150毫瓦);安卓iXon 897 EM-CCD相机。 4.软件:ZEN(黑色版),PALM模块。


      图2.采集参数的Zen设置(左)和示例图像(右)。左:1。激发激光和功率; 2.曲目; 3.发射滤波器; 4. TIRF设置和角度; 5.帧数; 6.曝光时间; 7. EM增益。 8.每个循环的框架。

    9. 使用ZEN软件中的PALM功能处理和渲染图像(图3)。使用ZEN首先定义点扩散函数(PSF)掩模大小(通常为9像素,最大10像素)和强度与噪声比(通常为6)然后应用于使用2D高斯拟合模型定位每个闪烁事件。&nbsp;
    10. 使用“重叠帐户”设置考虑重叠荧光团。&nbsp;
    11. 使用PAL-Drift选项卡中的特征检测和互相关方法,校正横向平面中分子的位移。
    12. 使用标准MultiSpec珠子样品实施重建图像的通道对齐(图4)。
    13. 最终重建图像中的局部化坐标(HER2通常为30,000+,每个区域为HER3为5,000+,图3)被传递到由Dylan Owen 等人设计的贝叶斯聚类识别算法> (Griffié et al。>,2016)。


      图3.数据分析参数的Zen设置。 1. PALM功能。 2. PSF掩模尺寸。 3.强度噪声比。 4.适合模特。 5.重叠帐户。 6. PAL漂移标签。 7.本地化和精确度的协调。


      图4.频道对齐设置

数据分析

  1. 贝叶斯聚类算法分析
    贝叶斯聚类算法用于确定聚类半径和聚类中的分子数(Griffié et al。>,2016)。尽管感兴趣区域(ROI)选择工具尚未针对Windows进行测试,但分析可以在Linux或Windows OS下完成。该软件需要python 2.7和R.这两个软件包,包括所有必需的库和模块,都是免费的开源软件,可用于Linux和Windows操作系统。安装取决于您的特定操作系统。在Ubuntu 16.04上,所有必需的软件都可以使用“apt install”命令安装,除了R库'splancs',可以通过运行R并输入'install.packages(“splancs”)','igraph'来安装。 '库也可以这种方式安装。可以使用python的'pip'工具安装所有必需的python模块。在第一次运行'roi_select.py'之前,在roi_selector文件夹中运行'pyrcc4 resources.qrc -o qrc_resources.py'。否则,工具按钮将具有文本标签而不是图标(视频1)。
    设置所需软件后,分析包含以下主要步骤。

    表1.使用贝叶斯聚类算法的数据分析示意图


    来自采集的不同通道的数据存储在具有相同基本名称的文件夹中,加上通道的扩展名“绿色”或“红色”(例如“s07a06green”和“s07a06red”用于数据集“s07a06”,请参阅还有视频1),文件夹名称需要以“绿色”或“红色”结束(注意这是关键步骤)这样做是为了帮助选择ROI(注意,该工具适用于1或2个通道)。否则,遵循协议的惯例(Griffié et al。>,2016)。聚类算法期望背景和聚类均匀分布在矩形ROI上。分析的图像主要显示单个细胞。标记的目标分子位于细胞膜中。细胞膜可见为大致圆形。为了符合聚类算法的先决条件,需要手动或半自动选择矩形区域。


    视频1.准备。输入数据的样子,没有图标的ROI选择器是什么样的,如何为ROI选择器编译python资源,以及ROI选择器的图标是什么样的。

    这是通过用Python编写的图形工具(提供)完成的。该工具显示采集的所有通道的数据,每个通道具有不同的颜色。确保矩形使用最合适的角度紧紧覆盖细胞膜。半自动程序可用于通过单个按钮优化手动选择的ROI(不需要矩形或紧密贴合膜),点击。通过ROI以这种方式覆盖整个细胞膜。由这些区域选择的数据自动旋转,以使矩形的边与坐标轴平行,并将所选数据写入文件,每个ROI一个文件夹。使用以下工作流程(图5,视频2):
    1. 打开数据文件,键“ctrl-L”或按钮(1)。
    2. 如果需要,选择绘图模式,按钮(4)。&nbsp;
    3. 开始绘图,按“d”或按钮(3)。
    4. 通过单击图像绘制矩形或多边形
    5. 选择3个点后,矩形将完成(基线为两点,第三个确定高度),单击按钮(3)或按“d”后,多边形将完成。将连接第一个和最后一个点以关闭多边形。
    6. 优化ROI,按钮(5)或按键's',这会将突出显示的ROI转换为包含突出显示的ROI最密集区域的矩形。
    7. 重复3-6。
    8. 保存矩形ROI(严重以注意不会保存多边形),按钮(2)或键'Ctrl + S'。第一次在每个会话中执行此操作时,将要求您选择输出文件夹。数据将存储在所选文件夹的子文件夹中。输出文件夹也可以从菜单中设置:“文件”→“设置基本输出目录”。
    9. 转到1。


    图5.感兴趣区域选择工具的功能。 打开数据集(1),保存矩形ROI(2),开始ROI图(3),选择ROI类型:多边形或矩形(4),自动优化选定的ROI(5),ROI(6),选中ROI(7),选项工具栏(8),Python的matplotlib工具栏(9),可编辑的消息窗口(10)。


    视频2. ROI选择工具的使用。如何打开数据集,如何激活散点图显示模式,如何缩放和平移,介绍主工具栏元素,如何使用多边形选择工具,如何使用优化-ROI工具,如何保存选定的数据和生成的输出文件夹的内容。

    可以随时更改绘图模式,可以删除ROI,并且可以显示有关ROI(观察次数,密度)的信息。该工具也将编写所需的配置文件。结果是一系列文件夹,例如(对于Linux OS):
    1)&lt; base-output-directory&gt; / s07a06r23415-14057_1466x3564_25red
    2)&lt; base-output-directory&gt; / s07a06r23415-14057_1466x3564_25green
    3)&lt; base-output-directory&gt; / s07a06r18906-21057_1101x2745_76red
    4)&lt; base-output-directory&gt; / s07a06r18906-21057_1101x2745_76green
    5)......
    每个文件夹包含两个文件:
    1)config.txt
    2)1 / data.txt
    注意:通道可能不包含特定ROI中的任何数据。在这种情况下,不为通道写入输出。 注意,频道彼此独立处理。>
    &NBSP;这些文件用作聚类算法的输入,并且如协议(Griffié等人>,2016)(视频3)所述应用算法。原始源文件(Griffié et al。>,2016中的补充信息)已经过修改(提供)以促进批量分析。通过运行执行分析:

    R -f run.R --args --target = &lt; data-folder&gt ; --config = =&lt; path -config-file&gt;

    从包含“run.R”脚本的文件夹中。这里,&lt; data-folder&gt; 是包含来自ROI的数据的文件夹(例如'&lt; base-output-directory&gt; / s07a06r18906-21057_1101x2745_76red')和&nbsp;&lt; ; path-to-config-file&gt;&nbsp; 是属于数据的'config.txt'的路径(此处为'&lt; base-output-directory&gt; /s07a06r18906-21057_1101x2745_76red/config.txt')。由于文件夹的数量可以很大,因此可以使用计算集群。我们提供了一个如何使用LSF作业调度程序在集群上运行脚本的示例。

    完成“run.R”分析后,&lt; data-folder&gt;&nbsp; 包含:
    1)config.txt
    2)1 / data.txt
    3)1 / all_labels.txt.gz
    4)1 / r_vs_thresh.txt
    使用'postprocessing.R'脚本(视频3)处理文件:

    R --slave -f postprocessing.R --args -folders = &lt; data -folder&gt;

    再次&nbsp;&lt; data-folder&gt;&nbsp; 是包含ROI数据的文件夹。此脚本将三个文件添加到&lt; data-folder&gt;&nbsp;
    1)1 / summary.txt
    2)radii.txt
    3)nmols.txt
    其中'radii.txt'包含簇大小列表,'nmols.txt'包含每簇的分子列表,这是所需的结果。


    视频3.在命令行上运行群集算法。如何使用README文件中的信息,如何编辑命令行,如何运行主聚类算法,算法的结果,如何运行后处理,后期处理的结果。

  2. 使用Clus DoC分析
    1. 为了进一步图像分析以比较HER2和HER3之间的共定位程度,使用不同的处理方法,使用在MATLAB环境中运行的Clus-DoC(具有共定位度的簇检测)程序分析dSTORM图像(Pageon et al。 >,2016)。此数据分析过程的示意图可以在Pageon et al。>(2016)的图3中找到。
    2. 图形用户界面(GUI)在MATLAB中使用命令行'ClusDoC'打开(图6),并且先前保存的ZEISS ZEN软件本地化表(文本文件格式)为每个图像加载为'输入文件'( A )。
    3. 然后使用“设置输出路径”按钮( B )选择要生成的数据的目的地。&nbsp;
    4. 使用“添加ROI”工具( C )在膜周围绘制,然后双击激活,从而选择整个细胞的膜。&nbsp;
    5. 点击“导出投资回报率”按钮( D )即可保存投资回报率。
    6. 首先进行Ripley K检验( E )以获得每个细胞的最大 L(r)-r >值(显示在'RipleyK图上')。该值作为后续DBSCAN和Clus-DoC测试的L(r)-r参数输入,以滤除噪声点。&nbsp;
    7. 然后在每个小区上运行多个DBSCAN测试( F )以确定获得与贝叶斯群集识别算法预测的一致的平均群集区域所需的参数。&nbsp;
    8. 记录最终参数,然后输入Clus-DoC测试参数,并对每个细胞进行Clus-DoC测试( G )以量化HER2和HER3之间的共定位程度。


      图6. ClusDoC GUI,其中加载了本地化表并选择了ROI。 GUI的按钮通过标签 A-G 链接到分析过程中的相关步骤。

    9. 对每个图像重复该过程。
    10. 然后在处理组之间比较生成的Excel结果表(在指定的输出路径文件夹中)中的簇大小和共定位百分比(参见Pageon et al。>,2016;表1中的完整列表)等属性。
    11. 结果可以在Claus 等人>(2018)中找到。

食谱

  1. 磷酸盐缓冲盐水(PBS)
    137 mM NaCl
    2.7 mM KCl
    10mM Na 2 HPO 4
    1.8mM KH 2 PO 4
    将pH调节至7.4并高压灭菌以消毒(121°C,15分钟)
    将PBS储存在4°C
  2. MEA缓冲区
    在PBS中加入50mM半胱胺盐酸盐(从-20℃储存的100mM储备液中稀释) 立即使用稀释的50 mM工作溶液

致谢

这项工作由医学研究委员会的MRC资助(参考MR / K015591 / 1)和生物技术与生物科学研究委员会的BBSRC资助BB / G006911 / 1资助。这项工作还得到了弗朗西斯克里克研究所的部分支持,该研究所获得了英国癌症研究中心(FC0010130),英国医学研究理事会(FC0010130)和威康信托基金(FC0010130)的核心资金。

利益争夺

作者声明没有利益冲突或竞争利益。

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Copyright Roberts et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Roberts, S. K., Hirsch, M., McStea, A., Zanetti-Domingues, L. C., Clarke, D. T., Claus, J., Parker, P. J., Wang, L. and Martin-Fernandez, M. L. (2018). Cluster Analysis of Endogenous HER2 and HER3 Receptors in SKBR3 Cells. Bio-protocol 8(23): e3096. DOI: 10.21769/BioProtoc.3096.
  2. Claus, J., Patel, G., Autore, F., Colomba, A., Weitsman, G., Soliman, T. N., Roberts, S., Zanetti-Domingues, L. C., Hirsch, M., Collu, F., George, R., Ortiz-Zapater, E., Barber, P. R., Vojnovic, B., Yarden, Y., Martin-Fernandez, M. L., Cameron, A., Fraternali, F., Ng, T. and Parker, P. J. (2018). Inhibitor-induced HER2-HER3 heterodimerisation promotes proliferation through a novel dimer interface. Elife 7: e32271.
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